Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions

TL;DR

This paper introduces a segmentation-guided MRI reconstruction method that generates diverse plausible images to quantify uncertainty, improving reliability over traditional probabilistic models.

Contribution

It proposes a novel diffusion-based MRI reconstruction approach guided by segmentation to produce meaningful diversity and uncertainty bounds.

Findings

Uncertainty boundary provides more reliable uncertainty quantification.

Diverse reconstructions improve downstream segmentation accuracy.

Method outperforms repeated sampling in uncertainty estimation.

Abstract

Inverse problems, such as accelerated MRI reconstruction, are ill-posed and an infinite amount of possible and plausible solutions exist. This may not only lead to uncertainty in the reconstructed image but also in downstream tasks such as semantic segmentation. This uncertainty, however, is mostly not analyzed in the literature, even though probabilistic reconstruction models are commonly used. These models can be prone to ignore plausible but unlikely solutions like rare pathologies. Building on MRI reconstruction approaches based on diffusion models, we add guidance to the diffusion process during inference, generating two meaningfully diverse reconstructions corresponding to an upper and lower bound segmentation. The reconstruction uncertainty can then be quantified by the difference between these bounds, which we coin the 'uncertainty boundary'. We analyzed the behavior of the upper and lower bound segmentations for a wide range of acceleration factors and found the uncertainty boundary to be both more reliable and more accurate compared to repeated sampling. Code is available at https://github.com/NikolasMorshuis/SGR